Recognizing fine-grained categories (e.g., bird species) is difficult due to the challenges of discriminative region localization and fine-grained feature learning. In this project, we are aiming at recognizing the fine-grained image categories at a very high accuracy. For example, now we can recognize more 1,000 flower species, 200 birds, 200 dogs, 800+ car models with the accuracy higher than 88% in several large-scale real-world datasets.
In our work accepted to CVPR 2017, we propose a novel recurrent attention convolutional neural network (RA-CNN) which recursively learns discriminative region attention and region-based feature representation at multiple scales in a mutually reinforced way. We also release Microsoft Flower Recognition (微软识花App) as an iOS application.
Figure: Fine-grained image recognition.
Figure: Microsoft Flower Recognition (“微软识花“)
People
Jianlong Fu
Senior Research Manager